ABSTRACT A cross-sectional twin design was used to study the developmental nature of genetic and environmental influences on morningness-eveningness (M-E). A total of 977 South Korean twin pairs aged 9-23 years completed 13 items of a Korean version of the Composite Scale through the telephone interview. The total sample was split into three age groups: preadolescents, adolescents, and young adults. Twin correlations did not vary significantly with age, suggesting that genetic influences on M-E are stable throughout the developmental span. Results of model-fitting analyses indicated that genetic and environmental factors explained, respectively, 45% and 55% of the variance in all three age groups. Environmental factors were primarily those factors that twins did not share as a consequence of their common rearing; family environmental factors in M-E were consistently near zero in all three age groups. The present study is the first to demonstrate genetic influences on M-E in preadolescent children as young as 9 years old. In spite of differences in culture and frequencies of genes between South Koreans and Caucasians, genetic and environmental influences on M-E found in the present sample were remarkably similar to those reported by previous studies on the basis of late adolescent and adult Caucasian twins.

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a b s t r a c t This study investigated the relationship between locomotion and morningness–eveningness. As locomo-tors strive for psychological movement towards goals, waking up early in the morning was hypothesized to match their concerns for immediate action. Participants were 342 psychology students (M age = 24.69) from the University of Rome ''La Sapienza'' who completed the locomotion scale and the morningness questionnaire. As hypothesized, results indicate that individuals who have a strong locomotion orienta-tion are more morning-oriented. Results are discussed with reference to regulatory mode theory.

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IntroductionOne of the never-ending debates in the developing field of sexual medicine is the extent to which genetics and experiences (i.e., “nature and nurture”) contribute to sexuality. The debate continues despite the fact that these two sides have different abilities to create a scientific environment to support their cause. Contemporary genetics has produced plenty of recent evidence, however, not always confirmed or sufficiently robust. On the other hand, the more traditional social theorists, frequently without direct evidence confirming their positions, criticize, sometimes with good arguments, the methods and results of the other side.AimThe aim of this article is to critically evaluate existent evidence that used genetic approaches to understand human sexuality.Methods
An expert in sexual medicine (E.A.J.), an expert in medical genetics (G.N.), and two experts in genetic epidemiology and quantitative genetics, with particular scientific experience in female sexual dysfunction (A.B.) and in premature ejaculation (P.J.), contributed to this review.Main Outcome MeasureExpert opinion supported by critical review of the currently available literature.ResultsThe existing literature on human sexuality provides evidence that many sexuality-related behaviors previously considered to be the result of cultural influences (such as mating strategies, attractiveness and sex appeal, propensity to fidelity or infidelity, and sexual orientation) or dysfunctions (such as premature ejaculation or female sexual dysfunction) seem to have a genetic component.Conclusions
Current evidence from genetic epidemiologic studies underlines the existence of biological and congenital factors regulating male and female sexuality. However, these relatively recent findings ask for replication in methodologically more elaborated studies. Clearly, increased research efforts are needed to further improve understanding the genetics of human sexuality. Jannini EA, Burri A, Jern P, and Novelli G. Genetics of human sexual behavior: Where we are, where we are going. Sex Med Rev **;**:**–**.

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Although prior research has established that eating behaviors are related to both the Big Five personality traits and time-of-day preference, no research has directly examined if time-of-day preference mediates personality differences in eating behavior. We directly tested this model by assessing participants’ (N = 279) Big Five personality traits, time-of-day preference, and three-factors of eating (i.e., restrained eating, uncontrolled eating, and emotional eating) using validated questionnaires. Mediation analyses revealed that time-of-day preference partially mediated the relationship between the personality factors (conscientiousness, neuroticism, and extraversion) and eating behavior, primarily uncontrolled eating. These results indicate that time-of-day preference, in part, accounts for personality differences in eating behavior. This emphasizes the need to assess time-of-day preference when examining the relationship between personality and health-related behaviors.

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Stability of genetic influence on morningness–eveningness: across-sectional examination of South Korean twins frompreadolescence to young adulthoodYOON-MI HURMedical Research Center, Seoul National University, Seoul, South KoreaAccepted in revised form 3 October 2006; received 18 February 2006ABSTRACTA cross-sectional twin design was used to study the developmental nature of genetic andenvironmental influences on morningness–eveningness (M–E). A total of 977 SouthKorean twin pairs aged 9–23 years completed 13 items of a Korean version of theComposite Scale through the telephone interview. The total sample was split into threeage groups: preadolescents, adolescents, and young adults. Twin correlations did notvary significantly with age, suggesting that genetic influences on M–E are stablethroughout the developmental span. Results of model-fitting analyses indicated thatgenetic and environmental factors explained, respectively, 45% and 55% of thevariance in all three age groups. Environmental factors were primarily those factorsthat twins did not share as a consequence of their common rearing; familyenvironmental factors in M–E were consistently near zero in all three age groups.The present study is the first to demonstrate genetic influences on M–E in preadolescentchildren as young as 9 years old. In spite of differences in culture and frequencies ofgenes between South Koreans and Caucasians, genetic and environmental influences onM–E found in the present sample were remarkably similar to those reported byprevious studies on the basis of late adolescent and adult Caucasian twins.keywordsness, twincircadian, development, environments, genetics, morningness-evening-Circadian rhythms are biological rhythms that display 24 hcyclic patterns of behavior. One of the most important aspectsof human individual differences in circadian rhythms has beenidentified as a degree of ?morningness? and ?eveningness?(Kerkhof, 1985; Merrow et al., 2005). Morningness–evening-ness (M–E) is mostly reflected in the preference in sleep–waketiming. Morning-type individuals get up easily and are morealert in the morning than in the evening, have a hard timesleeping late, fall asleep quickly in the evening, and preferdaytime activities. Evening-type individuals are more alert atnight, able to sleep late in the morning, take a long time to fallasleep at night, and prefer nighttime activities. It has beenreported that there is a significant relationship between M–Eand mental illness in particular, major depression, withdepressed patients having higher eveningness than normalcontrols (Drennan et al., 1991). M–E also has been associatedwith sleep disorders: morningness was related to difficulty inmaintaining sleep and the impossibility to return to sleep in theearly morning (sleep phase-advance syndrome); and evening-ness was related to difficulty in initiating sleep and morningsleepiness (Taillard et al., 2001).Previous studies have shown that with advancing age, thecircadian rhythms of many variables undergo changes. Typ-ically, preadolescent children tend to be morning-oriented. Butas children go through the transition from childhood toadolescence, they become evening-oriented (Carskadon et al.,1993; Gau and Soong, 2003; Kerkhof, 1985; Park et al., 2002;Roenneberg et al., 2004). In the Shinkoda et al. (2000) study,the authors administered an M–E questionnaire to over 500Japanese students aged 6–18 years, and found that scoressignificantly changed toward a preference for ?eveningness?Correspondence: Yoon-Mi Hur, Medical Research Center #110, SeoulNational University, College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110-799, South Korea. Tel.: 82-2-741-6179; fax: 82-2-741-6190; e-mail: ymhur@snu.ac.krJ. Sleep Res. (2007) 16, 17–23? 2007 European Sleep Research Society17

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over advancing grades; the change occurred most notablyaround the seventh grade.It has long been recognized that circadian rhythms arelargely determined by genetic factors. The twin method isparticularly useful for investigating genetic and environmentalinfluences on a trait variation within a population. The twinmethod decomposes the total variance of a trait into variancecomponents attributable to genetic and environmental factors.Genetic variance is further divided into additive and non-additive genetic variance components, whereas environmentalvariance is further decomposed into shared and non-sharedenvironmental variance components. Additive genetic variancerefers to genetic effects that simply add up across genes. Non-additive genetic variance involves the effects of interactionamong alleles at a single locus as well as interactions amongalleles at different loci. Shared environmental factors representthe environmental factors that are shared by two members of atwin pair. Examples of shared environmental factors includeparental socioeconomic status, parental childrearing styles andpractices, and effects of schools that two members of a twinpair attend together. Non-shared environmental factors rep-resent those environmental factors that are not shared by twomembers of a twin pair. Examples of non-shared environmen-tal factors include accidents and peers that the two members ofa twin pair do not share (Plomin et al., 1990).As the first twin study of M–E, Hur et al. (1998) analyzed310 pairs of adult reared-together and reared-apart MZ andDZ twins who completed an M–E questionnaire. Hur et al.(1998) found that genetic factors explained approximately54% of the total variance of M–E; age accounted for 3% of thetotal variance; and the remaining variance was attributable tonon-shared environmental influences and measurement error.Shared environmental effects on M–E were not significant inthe Hur et al. (1998) study.Vink et al. (2001) analyzed 1650 pairs of late adolescenttwins (mean age ¼ 17.8 years) and their parents (meanages ¼ 48.0 years for fathers and 46.0 years for mothers)and 124 pairs of adult twins (mean age ¼ 46.5 years) whoresponded to an M–E question with five answer categories. Inthe Vink et al. (2001) study, genetic influences on M–E were44% for the younger generation and 48% for the oldergeneration. They also demonstrated that non-additive geneticinfluences were significant (15% for the younger and 16% forthe older generation). In the Vink et al. (2001) study themagnitudes of genetic and environmental variances in M–Ewere not different between males and females. The Hur et al.(1998) and Vink et al. (2001) studies agree with each other inthat shared environmental influences were negligible in indi-vidual differences in M–E among late adolescents and adults,while genetic influences on M–E were substantial and com-parable in magnitude between late adolescents and adults.Whereas the Hur et al. and Vink et al. studies assessedgenetic and environmental contributions to M–E amongCaucasian twins who live in free societies, Klei et al. (2005)investigated genetic and environmental factors in M–E amongthe Hutterites, an endogamous, religious group whose mem-bers live under relatively fixed schedule in agrarian, self-supportive communities. In spite of the social restrictionimpinged upon the Hutterites, additive genetic influence onM–E among the Hutterites was found to be significant (23%)and only slightly lower than those estimated in the Dutch twinstudy by Vink et al. (2001). The results of the Klei et al. studysuggest that environmental constraints may not exert substan-tial influences on individual differences in M–E.The present study examined genetic and environmentalinfluences on M–E among South Korean children andadolescents who also live in relatively restrictive environments.Compared with most Western countries, South Korea main-tains a much stronger relationship between early academicachievements and later educational opportunities and socialstanding. Also, for the strong tradition of Confucianism in thesociety, excellent early academic achievements and subsequentperformance on the college entrance examination taken at theend of high school are considered family honor as well aspersonal accomplishments in South Korea. For these reasons,students in South Korea are expected to devote their timeprimarily to their studies. It is very common for most SouthKorean junior high school, high school, and even some of theelementary school students to stay at private educationalinstitutions or have tutoring until very late at night for extrastudy. This extremely competitive atmosphere and socialpressure significantly limit time available for sleep amongstudents and considerably affect the sleep/wake patterns ofstudents in South Korea. Investigators have reported thatSouth Korean students suffer from severe daytime sleepiness,sleep/wake problem behavior, and depressed-mood (Yanget al., 2005).The present study aimed to investigate whether geneticinfluence on M–E among South Korean twins is similar tothose found in Western samples. Especially, by studyingpreadolescent, adolescent, and young adult twins cross-sec-tionally, the present study attempted to determine whetherheritability estimates of M–E among South Koreans increase,decrease, or remain stable from preadolescence to youngadulthood. To my knowledge, the present study is the first toinclude the preadolescent period of the lifespan to identifygenetic influences on M–E.MATERIALS AND METHODSSampleThe sample consisted of 977 twin pairs who responded to atelephone survey conducted by ongoing South Korean TwinRegistry (SKTR) in the years 2004 and 2005. The SKTR is anationwide volunteer registry of South Korean twins and theirfamilies. A detailed description of the recruitment procedure ofthe SKTR was reported by Hur et al. (in press). The telephonesurveys in the years 2004 and 2005 were conducted mainly(>98%) for the twins who resided in Seoul, South Korea.Twins? zygosity in the SKTR was determined from the twins?parents? responses to a zygosity questionnaire that includes18 Y.-M. Hur? 2007 European Sleep Research Society, J. Sleep Res., 16, 17–23

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questions regarding physical similarities and frequency ofconfusion of the twins by family members and others. Twenty-two pairs were excluded from data analyses because theirzygosity was ambiguous.Table 1 provides a description of the sample. The totalsample ranged from 9 to 23 years of age and was split intothree age groups: preadolescents (9–12 years; 132 pairs of MZand 121 pairs of DZ twins), adolescents (13–18 years; 340 pairsof MZ and 177 pairs of DZ twins), and young adults (19–23 years; 194 pairs of MZ and 58 pairs of DZ twins). Thepreadolescent group in the present study consisted of element-ary school students (grades 4–6), the adolescent group, juniorhigh and high school students (grades 7–12), and the youngadult group, graduates of high schools.The number of MZ twins was not very different from that ofDZ twins in the preadolescent sample; however, the numbersof MZ twins exceeded the numbers of DZ twins in theadolescent and young adult samples. These cohort differencesin the rates of MZ and DZ twins largely reflect a change of thebirth rate of DZ twin pairs in South Korean population overthe past 20 years (Hur and Kwon, 2005) and do not necessarilyrepresent a serious ascertainment bias. In the preadolescentand adolescent samples, less than 55% of the sample wasfemale, suggesting that the samples consist of roughly equalnumbers of males and females. In the young adult sample,however, 63% of the sample was female. It appears that anoverrepresentation of females in the young adult sample ispartly because some of the male twins were in the militaryservice at the time of telephone interview because young adultmales in South Korea have the obligation for the army service.MeasureAs part of the regular SKTR telephone interview, twins wereasked to respond to a Korean version of the Composite Scale(CS) developed by Smith et al. (1989). The CS consists of 13items regarding preferred rising and bed times, preferred timesof physical and mental performance, and subjective alertnessafter rising and before going to bed, and subjective evaluationof morningness and eveningness. The items of the CS wereadapted from M–E Questionnaire (Horne and Ostberg, 1976)and Diurnal Type Scale (Torsvall and Akerstedt, 1980). TheCS yields scores on a single scale of morningness versuseveningness. Higher scores indicate greater ?morningness?,whereas lower scores represent greater ?eveningness?.The Korean version of the CS has been extensively studiedand shown to be reasonably reliable and valid (Kook et al.,1999; Yoon et al., 1997). In the present study, Cronbach areliabilities of the CS were 0.65 in preadolescents, 0.70 inadolescents, and 0.72 in young adults.Analytical proceduresTo estimate genetic and environmental influences on M–E inpreadolescents, adolescents, and young adults, intraclasscorrelations were computed for MZ and DZ twins in eachage group and biometrical model-fitting analyses were con-ducted. Because previous studies (e.g., Vink et al., 2001) haveshown no gender differences for the magnitude of genetic andenvironmental factors in M–E and because the sample size inthe present study is relatively small to perform analyses todetect gender differences in genetic and environmental influ-ences on M–E, correlational analyses and model-fitting ana-lyses were carried out on the basis of the combined sample ofmales and females. Prior to correlation and model-fittinganalyses, however, the scores of the CS were corrected forgender using a regression procedure (McGue and Bouchard,1984) to avoid possible bias arising from gender effects.Intraclass correlationsThe twin intraclass correlation was calculated using theformula r ¼ (MSB)MSW)/(MSB + MSW), where MSB andMSW are, respectively, the mean squares between and withinpairs estimated in a one-way analysis of variance (anova). Thehypotheses that the twin correlations were higher for MZ thanfor DZ twins and equal across the three age groups (pre-adolescents, adolescents, and young adults) were tested on thebasis of the Fisher z-transformation method that produces aheterogeneity chi-square test statistic (Donner and Rosner,1980).MZ twins share identical genes, whereas DZ twins share onaverage 50% of their segregating genes. For this reason,additive genetic effects are indicated if the correlation for MZpairs is greater than DZ pairs; and the importance of sharedenvironmental effects is indicated if the correlation for DZpairs is greater than half the correlation for MZ pairs. Becausenon-additive genetic effects involve allelic interactions, the DZtwin correlation would be less than half the MZ twincorrelation if non-additive genetic effects are important for atrait. Non-shared environmental effects are manifested aswithin MZ pair differences (i.e., 1)rMZ), because the effectsalways operate to make MZ twins dissimilar.Biometrical model-fittingUsing the software package Mx (Neale, 1999), additive genetic(A), shared environmental (C), non-additive genetic (D), andnon-shared environmental variance and measurement errorTable 1 Sample divided by age and zygosityCharacteristics Preadolescent Adolescent Young adult TotalNo. of MZ pairs 132No. of DZ pairsTotalM:F (%)Age (years)MeanSDRange340177 (89)51747:5314958 (15)20737:63621356 (171)97744:56121 (67)25345:5510.80.99–1215.41.713–1819.91.019–2315.23.49–23Numbers of opposite-sex DZ twin pairs are in parenthesis. MZ,monozygotic twins; DZ, dizygotic twins.Stability of genetic influence on morningness–eveningness 19? 2007 European Sleep Research Society, J. Sleep Res., 16, 17–23

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(E) parameters were estimated by the maximum likelihoodmethod. Fig. 1 illustrates a univariate behavioral geneticmodel that includes A, C, D, and E parameters. On the basisof the quantitative genetic theory, the A factors correlate 1.0and 0.5 for MZ and DZ twins, respectively, whereas the Dfactors correlate 1.0 and 0.25 for the corresponding twins.Because all twins were reared in the same family, thecorrelation for the C factors is 1.0 for both MZ and DZtwins. Twins are not correlated for the E factors for non-shared environmental factors are unique to each member of atwin pair. Because all four parameters (A, C, D, and E) cannotbe estimated in the same model, the ACE model and the ADEmodel were tested separately (Neale and Cardon, 1992).Models were fit to the raw data.Two steps were taken to complete the biometrical model-fitting analyses. Initially, models were fit to the twin data of allthree age groups separately in order to find the best-fitting,most parsimonious model within each age group. Fivedifferent models (ADE, ACE, AE, CE, and E) were testedagainst a null model within each age group.1The null modelwas a baseline model where variances of MZ and DZ twinswere allowed to differ, while variances of the first and thesecond twins were set to be equal. This model can serve as anull model because in all of the five models (ADE, ACE, AE,CE, and E), variances of MZ and DZ twins as well as those ofthe first and the second twins within each zygosity group wereconstrained to be equal.After the best-fitting model within each age group wasselected, the model was used as the basis for the constructionof the full and constrained models for the cross-sectionalanalyses. In the full model, the parameters were allowed todiffer across age groups, whereas in the constrained model, theparameters were set to be equal. Because the full modelimplicates that genetic and environmental variances vary withage, whereas the constrained model indicates stability with age,the comparison of the fit of the full and constrained modelswill tell us whether genetic and environmental influences onM–E are stable or change from preadolescence to youngadulthood.Two criteria were used in deciding on the best-fitting model:the chi-square difference test and the Akaike informationcriterion (AIC ¼ v2) 2df). When the raw data are used inmodel-fitting analyses, Mx computes minus twice the log-likelihood of the data ()2LL), with an arbitrary constant thatis a function of the data. If two models are nested, differencesin )2LL between nested models are distributed as a chi-square,with degrees of freedom as follows: dfk+1) dfk, where k isnumber of degrees of freedom (Bollen, 1989). A significantincrease in chi-square in the constrained model when com-pared with the full model would suggest that the constrainedmodel fit the data less well than the full model. A non-significant change in chi-square would indicate that the fullmodel and the constrained model are equally acceptable.When competing models were equally acceptable on thebasis of the chi-square difference test, a model that yielded thelowest AIC was chosen as the best-fitting model. AICquantifies the information content of a model in terms of thejoint criterion of fit and parsimony. Because the model-fittingthat results in the most information is that for which AIC isminimum (Akaike, 1987), the model that produces the lowestAIC was considered the best-fitting model.RESULTSDescriptive statisticsThe scores of the CS showed normal distribution (skew-ness ¼ 0.09; curtosis ¼ 0.02) in the present sample. Analysesof variance were conducted to test effects of age, gender,zygosity, and their interactions. Prior to anovas, the totalsample was split into two groups, each containing one twinfrom every pair, to correct for non-independence of observa-tions in twin pairs. In both twin groups, only age effectsattained statistical significance at P value of less than 0.001.The mean level of the CS progressively declined frompreadolescents to young adults, suggesting that twins becomemore evening-oriented with age. These age effects wereconsistent with the results from previous studies of M–Ementioned earlier (Gau and Soong, 2003; Kerkhof, 1985; Parket al., 2002; Roenneberg et al., 2004; Shinkoda et al., 2000).Twin correlations across age and zygosityTable 2 presents MZ and DZ twin intraclass correlations forthe CS separately by age group as well as pooled over ageECDAADCEr =1.0 for MZ, r =0.5 for DZr =1.0 for MZ, r =0.25 for DZ r =1.0 for MZ & DZTwin 1 Twin 2Figure 1. A univariate behavioral genetic model. Twin 1 and Twin 2represent scores of the CS for the first and the second twin, respect-ively. A, D, C, and E are latent variables representing, respectively,additive genetic, non-additive genetic, shared environmental, and non-shared environmental variance. Non-shared environmental varianceincludes measurement error. The curved, two-headed arrows indicatecorrelations between the variables they connect, and the one-headedarrows represent path, standardized partial regression of the measuredvariable on the latent variable.1The DE model was not tested because it has been argued thatdominance effects alone are not enough to explain the very low DZcorrelation when compared with MZ correlation and that overdom-inance occurs only in extreme gene frequencies (Eaves, 1988).20Y.-M. Hur? 2007 European Sleep Research Society, J. Sleep Res., 16, 17–23

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groups. Also reported in the table are the results of thestatistical comparisons of the correlation across age groupsand between the two zygosity groups. In none of the age-groupcomparisons did the chi-square test statistics achieve signifi-cance; that is, there is no evidence that twin similarity for theCS varies with age. In contrast, the MZ correlation exceededthe DZ correlation in all four groups (i.e., preadolescents,adolescents, young adults, and pooled group) with three of thefour comparisons being statistically significant. MZ correla-tions higher than twice the DZ correlations suggested theexistence of non-additive genetic influence on individualdifference in the CS. Taken together, analyses of twincorrelations showed that genetic influences on the variationof the CS tend to be constant among all three age groups.Biometrical model-fittingTable 3 summarizes the results of the model-fitting analyseswithin each age group. The first model is the null model wherevariances were allowed to differ between MZ and DZ twins.When variances were restricted to be equal between MZ andDZ twins, non-significant changes in v2occurred in the ADE,ACE, and AE models in preadolescents and adolescents,indicating that these three models are better than the nullmodel. The fact that the CE and E models were rejected inpreadolescents and adolescents on the basis of the chi-squaredifference test suggests that environmental effects alone cannotexplain individual differences in M–E in preadolescents andadolescents. Among the ADE, ACE, and AE models, AIC wasminimized in the ADE model in preadolescents, and in the AEmodel, in adolescents. These results indicate that the best-fitting model is the ADE model for preadolescents, and the AEmodel for adolescents.In young adults, non-significant changes in v2occurred inthe CE model in addition to the ADE, ACE, and AE modelswhen variances were fixed to be equal between MZ and DZtwins. It appears that due to the small sample size in youngadults, the superiority in fit between the AE and CE modelscould not be clearly determined by the chi-square differencetest alone. However, in the young adult group, the lowestAIC was found in the AE model, suggesting that the AEmodel is better than any other competing models for youngadults.On the basis of the conclusions drawn from the model-fittinganalyses within each age group, the full cross-sectional modelwas constructed with the A, D, and E parameters. Table 4provides the results of fitting the full model and its submodelsto the cross-sectional data, and genetic and environmentalestimates and their 95% confidence intervals for variousmodels. The full model (Model 1) included the A, D, and Eparameters for preadolescents, with A and E parametersvarying for adolescents and young adults. Next model (Model2) restricted the A and E parameters to be the same foradolescents and young adults, while maintaining the A, D, andE parameters for preadolescents. This procedure yielded anon-significant change in v2, suggesting that the magnitudes ofA and E effects are constant across adolescents and youngadults. Next, the D parameter was eliminated from Model 2and the A and E parameters were constrained to be equalacross the three age groups (Model 3). Again, the change in v2was not significant. Model 3 also had the lowest AIC value ofall models tested. Thus, Model 3 where additive genetic andnon-shared environmental variances were constrained to beequal from preadolescence to young adulthood was chosen asthe best, most parsimonious cross-sectional model for M–E. InModel 3, the additive genetic and non-shared environmentalinfluences were 45% (95% CI: 39–50%) and 55% (95% CI:50–61%), respectively. The results of model-fitting analyseswere in line with the conclusions drawn from an examinationof the twin correlations.Table 2 Intraclass correlations of the sex-corrected scores of theComposite Scale for MZ and DZ twins in three age groupsAge group correlationPreadolescent AdolescentYoung adultv2?Pooled rMZDZv2?0.470.0811.54**0.450.1810.76**0.470.272.220.3010.610.460.1625.46****P < 0.01. MZ, monozygotic twins; DZ, dizygotic twins.?Age group heterogeneity test (df ¼ 2).?Zygosity group heterogeneity test (df ¼ 1).Table 3 Results of model-fitting analyses within each age groupModelPreadolescents AdolescentsYoung adults)2LLdfv2Ddf AIC)2LLdfv2DdfAIC)2LLdfv2DdfAICNullADEACECEAEE1401.11401.31404.21414.01404.21434.04995025025035035042852.72852.82853.22865.12853.22934.31027103010301031103110321130.01130.91130.41131.71130.91174.44074104104114114120.23.112.9*3.132.9**33445)5.8)2.94.9)4.922.90.10.512.4*0.581.6**33445)5.9)5.54.4)7.571.60.90.41.70.944.3**33445)5.1)5.6)6.3)7.134.3*P < 0.05; **P < 0.01. AIC ¼ v2)2(Ddf). A ¼ additive genetic effects, D ¼ non-additive genetic effects, C ¼ shared environmental effects,E ¼ non-shared environmental effects and measurement error. The best-fitting, most parsimonious model in each age group is indicated inboldface.Stability of genetic influence on morningness–eveningness 21? 2007 European Sleep Research Society, J. Sleep Res., 16, 17–23